{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入所需库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib. pyplot as plt\n",
    "import seaborn\n",
    "import scipy.sparse as sp\n",
    "from statsmodels.regression.rolling import RollingOLS\n",
    "seaborn.set_style(\"darkgrid\")\n",
    "pd.plotting.register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarkettypeID</th>\n",
       "      <th>TradingDate</th>\n",
       "      <th>RiskPremium1</th>\n",
       "      <th>RiskPremium2</th>\n",
       "      <th>SMB1</th>\n",
       "      <th>SMB2</th>\n",
       "      <th>HML1</th>\n",
       "      <th>HML2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P9706</td>\n",
       "      <td>1990-12-19</td>\n",
       "      <td>2.473374</td>\n",
       "      <td>2.309671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P9710</td>\n",
       "      <td>1990-12-19</td>\n",
       "      <td>2.473374</td>\n",
       "      <td>2.309671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P9709</td>\n",
       "      <td>1990-12-19</td>\n",
       "      <td>2.473374</td>\n",
       "      <td>2.309671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P9712</td>\n",
       "      <td>1990-12-19</td>\n",
       "      <td>2.473374</td>\n",
       "      <td>2.309671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P9713</td>\n",
       "      <td>1990-12-19</td>\n",
       "      <td>2.473374</td>\n",
       "      <td>2.309671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MarkettypeID TradingDate  RiskPremium1  RiskPremium2  SMB1  SMB2  HML1  HML2\n",
       "0        P9706  1990-12-19      2.473374      2.309671   NaN   NaN   NaN   NaN\n",
       "1        P9710  1990-12-19      2.473374      2.309671   NaN   NaN   NaN   NaN\n",
       "2        P9709  1990-12-19      2.473374      2.309671   NaN   NaN   NaN   NaN\n",
       "3        P9712  1990-12-19      2.473374      2.309671   NaN   NaN   NaN   NaN\n",
       "4        P9713  1990-12-19      2.473374      2.309671   NaN   NaN   NaN   NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取所需因子数据（三因子模型）\n",
    "df=pd.read_csv('STK_MKT_THRFACDAY.csv',encoding='utf_8_sig')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarkettypeID</th>\n",
       "      <th>RiskPremium1</th>\n",
       "      <th>RiskPremium2</th>\n",
       "      <th>SMB1</th>\n",
       "      <th>SMB2</th>\n",
       "      <th>HML1</th>\n",
       "      <th>HML2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.010357</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.004638</td>\n",
       "      <td>0.004161</td>\n",
       "      <td>-0.010758</td>\n",
       "      <td>-0.011928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.010340</td>\n",
       "      <td>0.010769</td>\n",
       "      <td>-0.012563</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.016017</td>\n",
       "      <td>-0.014040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.004966</td>\n",
       "      <td>0.004517</td>\n",
       "      <td>-0.018080</td>\n",
       "      <td>-0.017969</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>0.003710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.007401</td>\n",
       "      <td>0.006454</td>\n",
       "      <td>-0.033309</td>\n",
       "      <td>-0.032130</td>\n",
       "      <td>-0.004250</td>\n",
       "      <td>-0.004629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>P9706</td>\n",
       "      <td>-0.002017</td>\n",
       "      <td>-0.001919</td>\n",
       "      <td>-0.005396</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>0.008191</td>\n",
       "      <td>0.008967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MarkettypeID  RiskPremium1  RiskPremium2      SMB1      SMB2  \\\n",
       "date                                                                      \n",
       "2021-01-04        P9706      0.010357      0.010930  0.004638  0.004161   \n",
       "2021-01-05        P9706      0.010340      0.010769 -0.012563 -0.011595   \n",
       "2021-01-06        P9706      0.004966      0.004517 -0.018080 -0.017969   \n",
       "2021-01-07        P9706      0.007401      0.006454 -0.033309 -0.032130   \n",
       "2021-01-08        P9706     -0.002017     -0.001919 -0.005396 -0.004850   \n",
       "\n",
       "                HML1      HML2  \n",
       "date                            \n",
       "2021-01-04 -0.010758 -0.011928  \n",
       "2021-01-05 -0.016017 -0.014040  \n",
       "2021-01-06  0.003920  0.003710  \n",
       "2021-01-07 -0.004250 -0.004629  \n",
       "2021-01-08  0.008191  0.008967  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#只取2021-01-01到2021-12-01之间MarkettypeID为P9706的数据\n",
    "df2=df[df.MarkettypeID=='P9706']\n",
    "df2.loc[:,['date']] = pd.to_datetime(df2['TradingDate'],format='%Y-%m-%d')\n",
    "df2 = df2.reset_index().set_index('date')\n",
    "df2 = df2.drop(['index','TradingDate'],axis=1)\n",
    "df2=df2.dropna()#去掉空值\n",
    "df3=df2.loc['2021-01-01':'2021-12-01']\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取相应的股票数据\n",
    "stocks = pd.read_csv('test.csv',encoding=\"utf_8\")\n",
    "stocks['date'] = pd.to_datetime(stocks['date'])\n",
    "stocks = stocks.reset_index().set_index('date')\n",
    "stocks = stocks.drop(['index'],axis=1)\n",
    "returns = np.log(stocks/stocks.shift(1))#求股票的对数收益率\n",
    "returns = returns.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>MarkettypeID</th>\n",
       "      <th>RiskPremium1</th>\n",
       "      <th>RiskPremium2</th>\n",
       "      <th>SMB1</th>\n",
       "      <th>SMB2</th>\n",
       "      <th>HML1</th>\n",
       "      <th>HML2</th>\n",
       "      <th>中国重汽</th>\n",
       "      <th>道恩股份</th>\n",
       "      <th>白云机场</th>\n",
       "      <th>东风汽车</th>\n",
       "      <th>中石化</th>\n",
       "      <th>中海油</th>\n",
       "      <th>中石油</th>\n",
       "      <th>上汽</th>\n",
       "      <th>比亚迪</th>\n",
       "      <th>on_b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.010340</td>\n",
       "      <td>0.010769</td>\n",
       "      <td>-0.012563</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.016017</td>\n",
       "      <td>-0.014040</td>\n",
       "      <td>-0.025414</td>\n",
       "      <td>0.061614</td>\n",
       "      <td>-0.000391</td>\n",
       "      <td>-0.016517</td>\n",
       "      <td>-0.037573</td>\n",
       "      <td>-0.002484</td>\n",
       "      <td>-0.036320</td>\n",
       "      <td>-0.041133</td>\n",
       "      <td>-0.002389</td>\n",
       "      <td>-0.209350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.004966</td>\n",
       "      <td>0.004517</td>\n",
       "      <td>-0.018080</td>\n",
       "      <td>-0.017969</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>0.003710</td>\n",
       "      <td>0.044676</td>\n",
       "      <td>-0.016507</td>\n",
       "      <td>0.048790</td>\n",
       "      <td>-0.019745</td>\n",
       "      <td>-0.046091</td>\n",
       "      <td>0.022141</td>\n",
       "      <td>0.011384</td>\n",
       "      <td>0.077767</td>\n",
       "      <td>0.016608</td>\n",
       "      <td>-0.179586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.007401</td>\n",
       "      <td>0.006454</td>\n",
       "      <td>-0.033309</td>\n",
       "      <td>-0.032130</td>\n",
       "      <td>-0.004250</td>\n",
       "      <td>-0.004629</td>\n",
       "      <td>0.043059</td>\n",
       "      <td>0.039612</td>\n",
       "      <td>-0.044892</td>\n",
       "      <td>-0.053862</td>\n",
       "      <td>-0.005914</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>0.011256</td>\n",
       "      <td>0.038806</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.410915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>P9706</td>\n",
       "      <td>-0.002017</td>\n",
       "      <td>-0.001919</td>\n",
       "      <td>-0.005396</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>0.008191</td>\n",
       "      <td>0.008967</td>\n",
       "      <td>-0.005023</td>\n",
       "      <td>0.010916</td>\n",
       "      <td>0.014294</td>\n",
       "      <td>0.026914</td>\n",
       "      <td>-0.027663</td>\n",
       "      <td>0.016848</td>\n",
       "      <td>0.095193</td>\n",
       "      <td>0.018513</td>\n",
       "      <td>0.009368</td>\n",
       "      <td>0.083382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>P9706</td>\n",
       "      <td>-0.011561</td>\n",
       "      <td>-0.011943</td>\n",
       "      <td>-0.015109</td>\n",
       "      <td>-0.013678</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.028327</td>\n",
       "      <td>0.031795</td>\n",
       "      <td>0.051581</td>\n",
       "      <td>-0.023025</td>\n",
       "      <td>-0.030962</td>\n",
       "      <td>-0.031518</td>\n",
       "      <td>0.033862</td>\n",
       "      <td>-0.015058</td>\n",
       "      <td>-0.016452</td>\n",
       "      <td>0.285179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MarkettypeID  RiskPremium1  RiskPremium2      SMB1      SMB2  \\\n",
       "date                                                                      \n",
       "2021-01-05        P9706      0.010340      0.010769 -0.012563 -0.011595   \n",
       "2021-01-06        P9706      0.004966      0.004517 -0.018080 -0.017969   \n",
       "2021-01-07        P9706      0.007401      0.006454 -0.033309 -0.032130   \n",
       "2021-01-08        P9706     -0.002017     -0.001919 -0.005396 -0.004850   \n",
       "2021-01-11        P9706     -0.011561     -0.011943 -0.015109 -0.013678   \n",
       "\n",
       "                HML1      HML2      中国重汽      道恩股份      白云机场      东风汽车  \\\n",
       "date                                                                     \n",
       "2021-01-05 -0.016017 -0.014040 -0.025414  0.061614 -0.000391 -0.016517   \n",
       "2021-01-06  0.003920  0.003710  0.044676 -0.016507  0.048790 -0.019745   \n",
       "2021-01-07 -0.004250 -0.004629  0.043059  0.039612 -0.044892 -0.053862   \n",
       "2021-01-08  0.008191  0.008967 -0.005023  0.010916  0.014294  0.026914   \n",
       "2021-01-11  0.000027  0.001391  0.028327  0.031795  0.051581 -0.023025   \n",
       "\n",
       "                 中石化       中海油       中石油        上汽       比亚迪      on_b  \n",
       "date                                                                    \n",
       "2021-01-05 -0.037573 -0.002484 -0.036320 -0.041133 -0.002389 -0.209350  \n",
       "2021-01-06 -0.046091  0.022141  0.011384  0.077767  0.016608 -0.179586  \n",
       "2021-01-07 -0.005914  0.002430  0.011256  0.038806  0.000000  0.410915  \n",
       "2021-01-08 -0.027663  0.016848  0.095193  0.018513  0.009368  0.083382  \n",
       "2021-01-11 -0.030962 -0.031518  0.033862 -0.015058 -0.016452  0.285179  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将因子数据与股票收益数据拼接到一起\n",
    "result = pd.concat([df3,returns],axis=1,join='inner')\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>道恩股份</td>       <th>  R-squared:         </th> <td>   0.423</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   52.77</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 31 Mar 2022</td> <th>  Prob (F-statistic):</th> <td>1.25e-25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:17:10</td>     <th>  Log-Likelihood:    </th> <td>  479.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   220</td>      <th>  AIC:               </th> <td>  -950.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   216</td>      <th>  BIC:               </th> <td>  -936.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>    0.0014</td> <td>    0.002</td> <td>    0.744</td> <td> 0.458</td> <td>   -0.002</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RiskPremium1</th> <td>    1.6093</td> <td>    0.217</td> <td>    7.400</td> <td> 0.000</td> <td>    1.181</td> <td>    2.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SMB1</th>         <td>    0.0712</td> <td>    0.228</td> <td>    0.312</td> <td> 0.755</td> <td>   -0.378</td> <td>    0.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HML1</th>         <td>   -1.8387</td> <td>    0.258</td> <td>   -7.134</td> <td> 0.000</td> <td>   -2.347</td> <td>   -1.331</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.000</td> <th>  Durbin-Watson:     </th> <td>   2.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.011</td> <th>  Jarque-Bera (JB):  </th> <td>   8.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.454</td> <th>  Prob(JB):          </th> <td>  0.0116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.385</td> <th>  Cond. No.          </th> <td>    150.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   道恩股份   R-squared:                       0.423\n",
       "Model:                            OLS   Adj. R-squared:                  0.415\n",
       "Method:                 Least Squares   F-statistic:                     52.77\n",
       "Date:                Thu, 31 Mar 2022   Prob (F-statistic):           1.25e-25\n",
       "Time:                        20:17:10   Log-Likelihood:                 479.10\n",
       "No. Observations:                 220   AIC:                            -950.2\n",
       "Df Residuals:                     216   BIC:                            -936.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const            0.0014      0.002      0.744      0.458      -0.002       0.005\n",
       "RiskPremium1     1.6093      0.217      7.400      0.000       1.181       2.038\n",
       "SMB1             0.0712      0.228      0.312      0.755      -0.378       0.521\n",
       "HML1            -1.8387      0.258     -7.134      0.000      -2.347      -1.331\n",
       "==============================================================================\n",
       "Omnibus:                        9.000   Durbin-Watson:                   2.101\n",
       "Prob(Omnibus):                  0.011   Jarque-Bera (JB):                8.921\n",
       "Skew:                           0.454   Prob(JB):                       0.0116\n",
       "Kurtosis:                       3.385   Cond. No.                         150.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=result[['RiskPremium1','SMB1','HML1']]\n",
    "y=result['道恩股份']\n",
    "X=sm.add_constant(x)#添加常数项\n",
    "model = sm.OLS(y,X)#OLS回归\n",
    "results = model.fit()\n",
    "results.params\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: \n",
    "1. Using Fama-French model to test the stocks in stocks50.csv, find the stocks with 5 higheset alpha;\n",
    "2. Expain your model economically,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fama-Macbeth Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FM method to jointly estimate the market price of factor risk A and the amount of factor risk exposure $\\beta$\n",
    "$$R=r_f+\\sum_i \\beta_i\\lambda_i+e_i$$\n",
    "Stage I: time-series to find risk exposure $\\hat{\\beta}_i$\n",
    "\n",
    "Stage II: panel-regression to find risk premium $\\hat{\\lambda}_i$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split using both named colums and ix for larger blocks\n",
    "#dates = result['date'].values\n",
    "factors = result[[ 'SMB1', 'HML1']].values\n",
    "riskfree =result['RiskPremium1'].values\n",
    "portfolios = result.iloc[:, 8:].values#各股票收益率数据\n",
    "\n",
    "# Use mat for easier linear algebra\n",
    "factors = np.mat(factors)\n",
    "riskfree = np.mat(riskfree)\n",
    "portfolios = np.mat(portfolios)\n",
    "\n",
    "# Shape information\n",
    "T,K = factors.shape\n",
    "T,N = portfolios.shape\n",
    "\n",
    "# Reshape rf and compute excess returns\n",
    "riskfree.shape = T,1\n",
    "excessReturns = portfolios - riskfree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time series regressions\n",
    "X = sm.add_constant(factors)\n",
    "ts_res = sm.OLS(excessReturns, X).fit()\n",
    "alpha = ts_res.params[0]\n",
    "beta = ts_res.params[1:]#每一个股票对每一个因子的敏感性 2*9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00020074,  0.00012014])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-section regression\n",
    "avgExcessReturns = np.mean(excessReturns, 0)#对每一列股票数据分别求平均值 1*9\n",
    "cs_res = sm.OLS(avgExcessReturns.T, beta.T).fit()\n",
    "riskPremia = cs_res.params\n",
    "riskPremia #得到两因子的风险溢价"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rolling Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rolling OLS applies OLS across a fixed windows of observations and then rolls (moves or slides) the window across the data set. They key parameter is window which determines the number of observations used in each OLS regression. By default, RollingOLS drops missing values in the window and so will estimate the model using the available data points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify window=60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarkettypeID</th>\n",
       "      <th>RiskPremium1</th>\n",
       "      <th>RiskPremium2</th>\n",
       "      <th>SMB1</th>\n",
       "      <th>SMB2</th>\n",
       "      <th>HML1</th>\n",
       "      <th>HML2</th>\n",
       "      <th>中国重汽</th>\n",
       "      <th>道恩股份</th>\n",
       "      <th>白云机场</th>\n",
       "      <th>东风汽车</th>\n",
       "      <th>中石化</th>\n",
       "      <th>中海油</th>\n",
       "      <th>中石油</th>\n",
       "      <th>上汽</th>\n",
       "      <th>比亚迪</th>\n",
       "      <th>on_b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.010340</td>\n",
       "      <td>0.010769</td>\n",
       "      <td>-0.012563</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.016017</td>\n",
       "      <td>-0.014040</td>\n",
       "      <td>-0.025414</td>\n",
       "      <td>0.061614</td>\n",
       "      <td>-0.000391</td>\n",
       "      <td>-0.016517</td>\n",
       "      <td>-0.037573</td>\n",
       "      <td>-0.002484</td>\n",
       "      <td>-0.036320</td>\n",
       "      <td>-0.041133</td>\n",
       "      <td>-0.002389</td>\n",
       "      <td>-0.209350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.004966</td>\n",
       "      <td>0.004517</td>\n",
       "      <td>-0.018080</td>\n",
       "      <td>-0.017969</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>0.003710</td>\n",
       "      <td>0.044676</td>\n",
       "      <td>-0.016507</td>\n",
       "      <td>0.048790</td>\n",
       "      <td>-0.019745</td>\n",
       "      <td>-0.046091</td>\n",
       "      <td>0.022141</td>\n",
       "      <td>0.011384</td>\n",
       "      <td>0.077767</td>\n",
       "      <td>0.016608</td>\n",
       "      <td>-0.179586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>P9706</td>\n",
       "      <td>0.007401</td>\n",
       "      <td>0.006454</td>\n",
       "      <td>-0.033309</td>\n",
       "      <td>-0.032130</td>\n",
       "      <td>-0.004250</td>\n",
       "      <td>-0.004629</td>\n",
       "      <td>0.043059</td>\n",
       "      <td>0.039612</td>\n",
       "      <td>-0.044892</td>\n",
       "      <td>-0.053862</td>\n",
       "      <td>-0.005914</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>0.011256</td>\n",
       "      <td>0.038806</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.410915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>P9706</td>\n",
       "      <td>-0.002017</td>\n",
       "      <td>-0.001919</td>\n",
       "      <td>-0.005396</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>0.008191</td>\n",
       "      <td>0.008967</td>\n",
       "      <td>-0.005023</td>\n",
       "      <td>0.010916</td>\n",
       "      <td>0.014294</td>\n",
       "      <td>0.026914</td>\n",
       "      <td>-0.027663</td>\n",
       "      <td>0.016848</td>\n",
       "      <td>0.095193</td>\n",
       "      <td>0.018513</td>\n",
       "      <td>0.009368</td>\n",
       "      <td>0.083382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>P9706</td>\n",
       "      <td>-0.011561</td>\n",
       "      <td>-0.011943</td>\n",
       "      <td>-0.015109</td>\n",
       "      <td>-0.013678</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.028327</td>\n",
       "      <td>0.031795</td>\n",
       "      <td>0.051581</td>\n",
       "      <td>-0.023025</td>\n",
       "      <td>-0.030962</td>\n",
       "      <td>-0.031518</td>\n",
       "      <td>0.033862</td>\n",
       "      <td>-0.015058</td>\n",
       "      <td>-0.016452</td>\n",
       "      <td>0.285179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MarkettypeID  RiskPremium1  RiskPremium2      SMB1      SMB2  \\\n",
       "date                                                                      \n",
       "2021-01-05        P9706      0.010340      0.010769 -0.012563 -0.011595   \n",
       "2021-01-06        P9706      0.004966      0.004517 -0.018080 -0.017969   \n",
       "2021-01-07        P9706      0.007401      0.006454 -0.033309 -0.032130   \n",
       "2021-01-08        P9706     -0.002017     -0.001919 -0.005396 -0.004850   \n",
       "2021-01-11        P9706     -0.011561     -0.011943 -0.015109 -0.013678   \n",
       "\n",
       "                HML1      HML2      中国重汽      道恩股份      白云机场      东风汽车  \\\n",
       "date                                                                     \n",
       "2021-01-05 -0.016017 -0.014040 -0.025414  0.061614 -0.000391 -0.016517   \n",
       "2021-01-06  0.003920  0.003710  0.044676 -0.016507  0.048790 -0.019745   \n",
       "2021-01-07 -0.004250 -0.004629  0.043059  0.039612 -0.044892 -0.053862   \n",
       "2021-01-08  0.008191  0.008967 -0.005023  0.010916  0.014294  0.026914   \n",
       "2021-01-11  0.000027  0.001391  0.028327  0.031795  0.051581 -0.023025   \n",
       "\n",
       "                 中石化       中海油       中石油        上汽       比亚迪      on_b  \n",
       "date                                                                    \n",
       "2021-01-05 -0.037573 -0.002484 -0.036320 -0.041133 -0.002389 -0.209350  \n",
       "2021-01-06 -0.046091  0.022141  0.011384  0.077767  0.016608 -0.179586  \n",
       "2021-01-07 -0.005914  0.002430  0.011256  0.038806  0.000000  0.410915  \n",
       "2021-01-08 -0.027663  0.016848  0.095193  0.018513  0.009368  0.083382  \n",
       "2021-01-11 -0.030962 -0.031518  0.033862 -0.015058 -0.016452  0.285179  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据回顾\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中国重汽</th>\n",
       "      <th>道恩股份</th>\n",
       "      <th>白云机场</th>\n",
       "      <th>东风汽车</th>\n",
       "      <th>中石化</th>\n",
       "      <th>中海油</th>\n",
       "      <th>中石油</th>\n",
       "      <th>上汽</th>\n",
       "      <th>比亚迪</th>\n",
       "      <th>on_b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>31.88</td>\n",
       "      <td>206.76</td>\n",
       "      <td>25.61</td>\n",
       "      <td>14.04</td>\n",
       "      <td>9.22</td>\n",
       "      <td>4.03</td>\n",
       "      <td>23.55</td>\n",
       "      <td>13.40</td>\n",
       "      <td>4.19</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>31.08</td>\n",
       "      <td>219.90</td>\n",
       "      <td>25.60</td>\n",
       "      <td>13.81</td>\n",
       "      <td>8.88</td>\n",
       "      <td>4.02</td>\n",
       "      <td>22.71</td>\n",
       "      <td>12.86</td>\n",
       "      <td>4.18</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>32.50</td>\n",
       "      <td>216.30</td>\n",
       "      <td>26.88</td>\n",
       "      <td>13.54</td>\n",
       "      <td>8.48</td>\n",
       "      <td>4.11</td>\n",
       "      <td>22.97</td>\n",
       "      <td>13.90</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>33.93</td>\n",
       "      <td>225.04</td>\n",
       "      <td>25.70</td>\n",
       "      <td>12.83</td>\n",
       "      <td>8.43</td>\n",
       "      <td>4.12</td>\n",
       "      <td>23.23</td>\n",
       "      <td>14.45</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>33.76</td>\n",
       "      <td>227.51</td>\n",
       "      <td>26.07</td>\n",
       "      <td>13.18</td>\n",
       "      <td>8.20</td>\n",
       "      <td>4.19</td>\n",
       "      <td>25.55</td>\n",
       "      <td>14.72</td>\n",
       "      <td>4.29</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             中国重汽    道恩股份   白云机场   东风汽车   中石化   中海油    中石油     上汽   比亚迪  on_b\n",
       "date                                                                         \n",
       "2021-01-04  31.88  206.76  25.61  14.04  9.22  4.03  23.55  13.40  4.19  0.90\n",
       "2021-01-05  31.08  219.90  25.60  13.81  8.88  4.02  22.71  12.86  4.18  0.73\n",
       "2021-01-06  32.50  216.30  26.88  13.54  8.48  4.11  22.97  13.90  4.25  0.61\n",
       "2021-01-07  33.93  225.04  25.70  12.83  8.43  4.12  23.23  14.45  4.25  0.92\n",
       "2021-01-08  33.76  227.51  26.07  13.18  8.20  4.19  25.55  14.72  4.29  1.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()#股票价格数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中国重汽_SMB1</th>\n",
       "      <th>道恩股份_SMB1</th>\n",
       "      <th>白云机场_SMB1</th>\n",
       "      <th>东风汽车_SMB1</th>\n",
       "      <th>中石化_SMB1</th>\n",
       "      <th>中海油_SMB1</th>\n",
       "      <th>中石油_SMB1</th>\n",
       "      <th>上汽_SMB1</th>\n",
       "      <th>比亚迪_SMB1</th>\n",
       "      <th>中国重汽_HML1</th>\n",
       "      <th>道恩股份_HML1</th>\n",
       "      <th>白云机场_HML1</th>\n",
       "      <th>东风汽车_HML1</th>\n",
       "      <th>中石化_HML1</th>\n",
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       "      <th>中石油_HML1</th>\n",
       "      <th>上汽_HML1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>1.027998</td>\n",
       "      <td>0.176893</td>\n",
       "      <td>0.696501</td>\n",
       "      <td>-0.061444</td>\n",
       "      <td>0.710752</td>\n",
       "      <td>0.255076</td>\n",
       "      <td>-0.119563</td>\n",
       "      <td>-0.045799</td>\n",
       "      <td>0.018618</td>\n",
       "      <td>-0.620557</td>\n",
       "      <td>-1.868098</td>\n",
       "      <td>-0.151843</td>\n",
       "      <td>-0.292644</td>\n",
       "      <td>-0.173841</td>\n",
       "      <td>1.209608</td>\n",
       "      <td>-0.467376</td>\n",
       "      <td>1.210021</td>\n",
       "      <td>1.127248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1.054346</td>\n",
       "      <td>0.194626</td>\n",
       "      <td>0.724284</td>\n",
       "      <td>-0.108611</td>\n",
       "      <td>0.791977</td>\n",
       "      <td>0.249355</td>\n",
       "      <td>-0.125759</td>\n",
       "      <td>-0.073977</td>\n",
       "      <td>-0.013565</td>\n",
       "      <td>-0.633667</td>\n",
       "      <td>-1.877091</td>\n",
       "      <td>-0.167442</td>\n",
       "      <td>-0.269295</td>\n",
       "      <td>-0.219723</td>\n",
       "      <td>1.212653</td>\n",
       "      <td>-0.463112</td>\n",
       "      <td>1.225184</td>\n",
       "      <td>1.144511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>1.058435</td>\n",
       "      <td>0.215265</td>\n",
       "      <td>0.772479</td>\n",
       "      <td>-0.110266</td>\n",
       "      <td>0.861925</td>\n",
       "      <td>0.254070</td>\n",
       "      <td>-0.140698</td>\n",
       "      <td>-0.052474</td>\n",
       "      <td>-0.018411</td>\n",
       "      <td>-0.597323</td>\n",
       "      <td>-1.863301</td>\n",
       "      <td>-0.178308</td>\n",
       "      <td>-0.186569</td>\n",
       "      <td>-0.097423</td>\n",
       "      <td>1.218888</td>\n",
       "      <td>-0.475129</td>\n",
       "      <td>1.264982</td>\n",
       "      <td>1.137754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>1.152916</td>\n",
       "      <td>0.259593</td>\n",
       "      <td>0.780766</td>\n",
       "      <td>-0.209796</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.229990</td>\n",
       "      <td>-0.109451</td>\n",
       "      <td>-0.066687</td>\n",
       "      <td>-0.022300</td>\n",
       "      <td>-0.657677</td>\n",
       "      <td>-1.893531</td>\n",
       "      <td>-0.183191</td>\n",
       "      <td>-0.147841</td>\n",
       "      <td>-0.171873</td>\n",
       "      <td>1.225727</td>\n",
       "      <td>-0.502831</td>\n",
       "      <td>1.272676</td>\n",
       "      <td>1.132209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1.209850</td>\n",
       "      <td>0.275719</td>\n",
       "      <td>0.743403</td>\n",
       "      <td>-0.185293</td>\n",
       "      <td>0.948310</td>\n",
       "      <td>0.228492</td>\n",
       "      <td>-0.107892</td>\n",
       "      <td>-0.085319</td>\n",
       "      <td>-0.016763</td>\n",
       "      <td>-0.562058</td>\n",
       "      <td>-1.871847</td>\n",
       "      <td>-0.257665</td>\n",
       "      <td>-0.111457</td>\n",
       "      <td>-0.183328</td>\n",
       "      <td>1.231227</td>\n",
       "      <td>-0.487546</td>\n",
       "      <td>1.248511</td>\n",
       "      <td>1.155428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     中国重汽_SMB1  道恩股份_SMB1  白云机场_SMB1  东风汽车_SMB1  中石化_SMB1  中海油_SMB1  中石油_SMB1  \\\n",
       "1          NaN        NaN        NaN        NaN       NaN       NaN       NaN   \n",
       "2          NaN        NaN        NaN        NaN       NaN       NaN       NaN   \n",
       "3          NaN        NaN        NaN        NaN       NaN       NaN       NaN   \n",
       "4          NaN        NaN        NaN        NaN       NaN       NaN       NaN   \n",
       "5          NaN        NaN        NaN        NaN       NaN       NaN       NaN   \n",
       "..         ...        ...        ...        ...       ...       ...       ...   \n",
       "216   1.027998   0.176893   0.696501  -0.061444  0.710752  0.255076 -0.119563   \n",
       "217   1.054346   0.194626   0.724284  -0.108611  0.791977  0.249355 -0.125759   \n",
       "218   1.058435   0.215265   0.772479  -0.110266  0.861925  0.254070 -0.140698   \n",
       "219   1.152916   0.259593   0.780766  -0.209796  0.956522  0.229990 -0.109451   \n",
       "220   1.209850   0.275719   0.743403  -0.185293  0.948310  0.228492 -0.107892   \n",
       "\n",
       "      上汽_SMB1  比亚迪_SMB1  中国重汽_HML1  道恩股份_HML1  白云机场_HML1  东风汽车_HML1  中石化_HML1  \\\n",
       "1         NaN       NaN        NaN        NaN        NaN        NaN       NaN   \n",
       "2         NaN       NaN        NaN        NaN        NaN        NaN       NaN   \n",
       "3         NaN       NaN        NaN        NaN        NaN        NaN       NaN   \n",
       "4         NaN       NaN        NaN        NaN        NaN        NaN       NaN   \n",
       "5         NaN       NaN        NaN        NaN        NaN        NaN       NaN   \n",
       "..        ...       ...        ...        ...        ...        ...       ...   \n",
       "216 -0.045799  0.018618  -0.620557  -1.868098  -0.151843  -0.292644 -0.173841   \n",
       "217 -0.073977 -0.013565  -0.633667  -1.877091  -0.167442  -0.269295 -0.219723   \n",
       "218 -0.052474 -0.018411  -0.597323  -1.863301  -0.178308  -0.186569 -0.097423   \n",
       "219 -0.066687 -0.022300  -0.657677  -1.893531  -0.183191  -0.147841 -0.171873   \n",
       "220 -0.085319 -0.016763  -0.562058  -1.871847  -0.257665  -0.111457 -0.183328   \n",
       "\n",
       "     中海油_HML1  中石油_HML1   上汽_HML1  比亚迪_HML1  \n",
       "1         NaN       NaN       NaN       NaN  \n",
       "2         NaN       NaN       NaN       NaN  \n",
       "3         NaN       NaN       NaN       NaN  \n",
       "4         NaN       NaN       NaN       NaN  \n",
       "5         NaN       NaN       NaN       NaN  \n",
       "..        ...       ...       ...       ...  \n",
       "216  1.209608 -0.467376  1.210021  1.127248  \n",
       "217  1.212653 -0.463112  1.225184  1.144511  \n",
       "218  1.218888 -0.475129  1.264982  1.137754  \n",
       "219  1.225727 -0.502831  1.272676  1.132209  \n",
       "220  1.231227 -0.487546  1.248511  1.155428  \n",
       "\n",
       "[220 rows x 18 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#第一步回归 Time series regression\n",
    "#定义解释变量\n",
    "exog_vars = ['SMB1', 'HML1']\n",
    "exog = sm.add_constant(result[exog_vars])\n",
    "#数据准备\n",
    "Param_const=pd.DataFrame()\n",
    "Param_SMB1=pd.DataFrame()\n",
    "Param_HML1=pd.DataFrame()\n",
    "endog=pd.DataFrame()\n",
    "#一共9支股票\n",
    "for i in range(9):\n",
    "    col=stocks.columns[i]\n",
    "    endog[col] =result [col] - result ['RiskPremium1']#求超额收益率\n",
    "    rols = RollingOLS(endog[col], exog, window=60)#Rolling Regression\n",
    "    rres= rols.fit()\n",
    "    params= rres.params.copy()\n",
    "    params.index = np.arange(1, params.shape[0] + 1)\n",
    "    #分别获取相应系数\n",
    "    params_0=params['const']\n",
    "    params_1=params['SMB1']\n",
    "    params_2=params['HML1']\n",
    "    #将每支股票对相应因子的敏感度拼接起来\n",
    "    Param_const=pd.concat([Param_const,params_0],axis=1)\n",
    "    Param_SMB1=pd.concat([Param_SMB1,params_1],axis=1)\n",
    "    Param_HML1=pd.concat([Param_HML1,params_2],axis=1)\n",
    "Param_Stocks=pd.concat([ Param_SMB1,Param_HML1],axis=1)\n",
    "Param_Stocks.columns=['中国重汽_SMB1','道恩股份_SMB1','白云机场_SMB1','东风汽车_SMB1','中石化_SMB1','中海油_SMB1','中石油_SMB1','上汽_SMB1','比亚迪_SMB1','中国重汽_HML1','道恩股份_HML1','白云机场_HML1','东风汽车_HML1','中石化_HML1','中海油_HML1','中石油_HML1','上汽_HML1','比亚迪_HML1']\n",
    "Param_Stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#选取最后一支股票“比亚迪”进行回归结果展示\n",
    "fig = rres.plot_recursive_coefficient(variables=exog_vars, figsize=(14, 18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-2.58965787e-03, -3.50946599e-03, -9.29204668e-04, ...,\n",
       "          1.64094713e-03,  3.23370817e-04,  1.10738271e-02],\n",
       "        [-3.89653037e-03, -3.34971080e-03, -3.30882203e-04, ...,\n",
       "          2.51368786e-03,  6.51309678e-04,  1.55220541e-02],\n",
       "        [-3.58965575e-03, -4.02470958e-03, -2.62081272e-04, ...,\n",
       "          1.16241300e-03,  4.16986289e-04,  1.78475073e-02],\n",
       "        ...,\n",
       "        [ 9.60019417e-04,  2.24622437e-03,  2.23099093e-03, ...,\n",
       "          4.80054304e-04,  3.18319351e-05, -3.02332063e-03],\n",
       "        [ 7.43191802e-04,  2.43886849e-03,  1.98905747e-03, ...,\n",
       "          4.64619270e-04, -2.58204908e-04,  5.15096847e-04],\n",
       "        [ 1.05907874e-03,  1.74557528e-03,  2.70203449e-03, ...,\n",
       "         -1.99985702e-05, -2.66916667e-04,  1.12277682e-03]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对9支股票的超额收益率同样进行移动平均 window=60\n",
    "excessReturns = portfolios - riskfree #前面已定义\n",
    "Mean_excessReturns=np.mat(np.zeros((161,9)))#进行移动平均后数据只有161行非空\n",
    "for j in range(161):\n",
    "     Mean_excessReturns[j]= np.mean(excessReturns[j:j+60], 0)  \n",
    "Mean_excessReturns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-0.4715047 , -0.87793722,  0.57622692, ...,  0.60393537,\n",
       "          1.59502826,  1.14005406],\n",
       "        [-0.52736497, -0.86955216,  0.57302026, ...,  0.47541843,\n",
       "          1.43001737,  1.16070871],\n",
       "        [-0.40693094, -0.94974935,  0.75846144, ...,  0.44014696,\n",
       "          1.33657565,  1.13205855],\n",
       "        ...,\n",
       "        [ 1.05843535,  0.21526503,  0.77247874, ..., -0.47512861,\n",
       "          1.26498201,  1.13775392],\n",
       "        [ 1.15291578,  0.25959252,  0.78076648, ..., -0.50283126,\n",
       "          1.27267649,  1.13220943],\n",
       "        [ 1.20984992,  0.27571932,  0.74340292, ..., -0.48754562,\n",
       "          1.24851147,  1.15542804]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Param_Stocks=Param_Stocks.iloc[59:].values#只选取非空的数据\n",
    "Param_Stocks=np.mat(Param_Stocks)\n",
    "Param_Stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 6.95049950e-04,  9.25957503e-04],\n",
       "        [ 1.05574854e-03,  1.59899729e-03],\n",
       "        [ 1.26749437e-03,  1.85931410e-03],\n",
       "        [ 1.61207026e-03,  1.26088538e-03],\n",
       "        [ 1.52320461e-03,  1.21184564e-03],\n",
       "        [ 1.56479602e-03,  1.02333212e-03],\n",
       "        [ 1.53905583e-03,  9.85827317e-04],\n",
       "        [ 9.90493754e-04,  1.36540575e-03],\n",
       "        [ 1.00489251e-03,  1.09119250e-03],\n",
       "        [ 1.26904688e-03,  1.12460733e-03],\n",
       "        [ 1.10436078e-03,  8.44877128e-04],\n",
       "        [ 6.56765582e-04,  7.72477618e-04],\n",
       "        [ 7.00227475e-04,  7.17310876e-04],\n",
       "        [ 5.56643908e-04,  7.27725662e-04],\n",
       "        [ 6.39874279e-04,  9.13081972e-04],\n",
       "        [ 1.91446845e-04,  9.15941493e-04],\n",
       "        [-1.11836887e-04,  5.79358290e-04],\n",
       "        [-3.81163680e-04,  5.83853953e-04],\n",
       "        [-2.27015436e-04,  8.22698590e-04],\n",
       "        [-5.80584956e-04,  8.80527458e-04],\n",
       "        [ 1.82435709e-04,  1.15672595e-03],\n",
       "        [ 7.76575438e-04,  1.44238997e-03],\n",
       "        [ 6.85110442e-04,  1.92396076e-03],\n",
       "        [ 5.78231721e-04,  1.71806298e-03],\n",
       "        [ 1.11666376e-03,  1.63150364e-03],\n",
       "        [ 9.44275491e-04,  1.39587745e-03],\n",
       "        [ 1.12761478e-03,  1.80674340e-03],\n",
       "        [ 1.41741057e-03,  1.80626561e-03],\n",
       "        [ 3.24097858e-04,  1.22074007e-03],\n",
       "        [ 5.39902257e-05,  1.67965839e-03],\n",
       "        [-7.87392086e-05,  2.01579954e-03],\n",
       "        [-6.48632370e-04,  1.40261011e-03],\n",
       "        [-5.51944415e-04,  1.89918344e-03],\n",
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       "        [-1.40220910e-04,  1.98253386e-03],\n",
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       "        [-3.37218412e-03,  1.10356224e-03],\n",
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       "        [-6.74473659e-04,  7.39564717e-04],\n",
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       "        [-1.16928334e-03,  3.35063072e-04],\n",
       "        [-2.96974488e-04,  5.75678703e-04],\n",
       "        [-6.44812599e-04,  6.13467231e-04],\n",
       "        [-6.25790989e-04,  8.12948738e-04],\n",
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       "        [-2.64560073e-03, -7.91511924e-04],\n",
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       "        [-1.98912944e-03,  1.80274914e-04],\n",
       "        [-2.03113230e-03,  1.78950035e-04],\n",
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       "        [-2.26236411e-03,  4.04181339e-04],\n",
       "        [-3.04713978e-03, -5.83665682e-05],\n",
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       "        [-3.20572016e-03,  3.70356224e-04],\n",
       "        [-3.07643729e-03,  1.79552638e-04],\n",
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       "        [-3.03483918e-03,  7.31387490e-04],\n",
       "        [-2.73292725e-03,  6.31627118e-04],\n",
       "        [-2.18152621e-03,  3.48163321e-04],\n",
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       "        [-1.82815351e-03,  3.20075443e-04],\n",
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       "        [ 1.22754467e-04,  1.13408909e-03],\n",
       "        [ 5.67721031e-04,  1.32834384e-03],\n",
       "        [-5.04475541e-04,  9.33117040e-04],\n",
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       "        [-1.40593700e-03,  3.43285704e-04],\n",
       "        [-6.80996792e-04,  1.00894107e-03],\n",
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       "        [ 5.73067648e-04,  1.13276608e-03],\n",
       "        [-3.03657479e-04,  3.24714609e-04],\n",
       "        [ 2.55164513e-04,  8.43983999e-04],\n",
       "        [ 2.97577485e-04,  7.99527820e-04],\n",
       "        [ 1.34560991e-03,  9.80653674e-04],\n",
       "        [ 2.18403371e-03,  8.55905127e-04],\n",
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       "        [ 2.27776558e-03,  4.35402858e-04],\n",
       "        [ 1.06652321e-03, -1.33662645e-04],\n",
       "        [ 1.21472762e-03, -1.35505399e-04],\n",
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       "        [ 8.11467820e-04, -3.71426082e-04],\n",
       "        [ 2.00422693e-03,  2.27333870e-04],\n",
       "        [ 1.51188137e-03, -3.35261867e-04],\n",
       "        [ 2.17813470e-03,  4.66241146e-04],\n",
       "        [ 2.43733950e-03,  1.08262447e-03],\n",
       "        [ 2.09064581e-03,  7.11891437e-04],\n",
       "        [ 2.13478038e-03,  9.17824114e-04],\n",
       "        [ 1.96920516e-03,  1.11847107e-03],\n",
       "        [ 2.47045372e-03,  1.47865304e-03],\n",
       "        [ 2.80655043e-03,  1.37388800e-03],\n",
       "        [ 2.53437774e-03,  1.24709454e-03],\n",
       "        [ 2.11067853e-03,  1.52325787e-04],\n",
       "        [ 2.37511316e-03,  1.40791014e-04],\n",
       "        [ 2.37231819e-03,  3.26293461e-04],\n",
       "        [ 2.00380205e-03,  1.71250639e-04],\n",
       "        [ 2.15766300e-03,  2.39014655e-04],\n",
       "        [ 2.28362192e-03,  5.40807260e-04],\n",
       "        [ 1.94638500e-03,  3.89960351e-04],\n",
       "        [ 1.90717838e-03,  7.15976943e-04],\n",
       "        [ 1.91247058e-03,  5.78308798e-04],\n",
       "        [ 1.85059252e-03,  5.20898004e-04],\n",
       "        [ 1.58107600e-03,  1.49848537e-04],\n",
       "        [ 1.38469114e-03, -5.47162184e-05],\n",
       "        [ 1.14276089e-03, -3.90380629e-04],\n",
       "        [ 1.10201851e-03, -6.77801754e-04],\n",
       "        [ 1.00637500e-03, -3.06746700e-04],\n",
       "        [ 1.27568589e-03, -1.03760916e-04]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进行第二步回归 Cross-section regression\n",
    "roll_riskPremia=np.mat(np.zeros((161,2)))#数据准备\n",
    "for i in range(161):\n",
    "    roll_exog=np.vstack((Param_Stocks[i,0:9], Param_Stocks[i,9:19]))\n",
    "    roll_cs_res = sm.OLS(Mean_excessReturns[i].T, roll_exog.T).fit()#第二个参数是9*2  \n",
    "    roll_riskPremia[i] =  roll_cs_res.params\n",
    "roll_riskPremia #得到两因子的风险溢价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-0.00045101,  0.00063618]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fin_riskPremia=np.mean(roll_riskPremia , 0)#进行平均\n",
    "Fin_riskPremia"
   ]
  }
 ],
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